Abstract PR005: An atlas of cellular heterogeneity in primary and metastatic renal cell carcinomas
Notice bibliographique
Résumé
Abstract Renal cell carcinoma (RCC) tumors harbor various layers of intra-tumor heterogeneity at the genetic, transcriptomic, and cellular levels. A series of recent studies have underlined the importance of charting this cellular variability to understand the cell of origin of various RCCs and the mechanisms that underlie their response to therapy. However, the regulatory factors driving intra-tumor heterogeneity in RCCs, the role of this heterogeneity in driving metastasis, and its association with clinical outcomes are mostly unknown. To address these questions, we have generated a cellular map of transcriptional heterogeneity in RCCs, which consists of >100,000 single-cell expression profiles from six patient-derived xenografts (PDX) models, including models derived from primary and brain metastatic tumors, as well as ten primary and six metastatic patient tumor tissues. We also developed a computational method for Gene Expression Decomposition and Integration (GEDI) that enables seamless integration of single-cell transcriptomic data across heterogeneous cancer samples while providing interpretable axes of variation. Using GEDI, we performed a fine-grained analysis of the heterogeneity of cancer cells and found that, despite patient-specific differences, common sources of intra-tumor heterogeneity exist across samples, driven by variable activity of pathways such as TNF-α/NF-κB signaling and oxidative phosphorylation (OxPhos). Analysis of data from our PDX models confirmed that this heterogeneity was stable and reproducible. A major source of intra-tumor heterogeneity was hypoxia signaling, even in VHL-deficient clear cell RCC samples (ccRCC), challenging the traditional view that ccRCC cells have a uniform pseudo-hypoxic status due to VHL inactivation. Regulatory network activity projection allowed us to disentangle the contributions of Hypoxia Inducible Factors HIF1A and HIF2A, revealing their divergent regulatory programs: while HIF1A is associated with cell-cycle and proliferation signatures, the activity of HIF2A correlates with epithelial-mesenchymal transition (EMT) within tumors, which we validated by RNA sequencing of HIF1A and EPAS1 knockdown in VHL-negative cells. Next, we used GEDI to study the cell state changes that occur during metastasis in both neoplastic and non-neoplastic tumor-infiltrating cells. By examining the differences in gene expression patterns between primary and metastatic samples, we identified various pathways that are transcriptionally activated during this transition, including EMT and Oxphos. We also uncovered distinct subpopulations of cancer cells with high gene expression changes associated with a metastatic profile. Analysis of the ligand-receptor interactions in the tumor microenvironment revealed that interactions from malignant cells to immune cells were over-represented in metastatic tumors. Overall, our cellular atlas has uncovered a complex continuum of cell states in RCCs, highlighting various drivers of the intra-tumor heterogeneity and establishing various cell states associated with metastasis. Citation Format: Ariel Madrigal, Minjun Kim, Adrien Osakwe, Tianyuan Lu, Zohreh Mehrjoo, Elham Moslemi, Rick Farouni, Larisa Morales-Soto, Yu Chang Wang, Matthew Dankner, Haig Djambazian, Kevin Petrecca, Jonathan Spicer, Fadi Brimo, Peter Siegel, Morag Park, Jiannis Ragoussis, Simon Tanguay, Yasser Riazalhosseini, Hamed S. Najafabadi. An atlas of cellular heterogeneity in primary and metastatic renal cell carcinomas [abstract]. In: Proceedings of the AACR Special Conference: Advances in Kidney Cancer Research; 2023 Jun 24-27; Austin, Texas. Philadelphia (PA): AACR; Cancer Res 2023;83(16 Suppl):Abstract nr PR005.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».